Objective
To evaluate the effect of situational leadership theory training on head nurses’ leadership style in nursing management, and summarize the application methods of situational leadership theory.
Methods
In December 2013, by means of convenience cluster sampling method, 154 head nurses of West China Hospital of Sichuan University were selected for a Leadership Style Self-rating Questionnaire survey, of whom 84 attended the situational leadership theory training one month ago. The questionnaire score was compared between the trained head nurses (the trained group) and the non-trained ones (the non-trained group).
Results
A total of 154 questionnaires were issued, and 109 valid ones were recovered, in whom 72 were trained by the situational leadership theory while the other 37 were not. The average scores of head nurses’ flexibility and efficacy in the trained group (22.35±5.12 and 55.67±7.59) were higher than those in the non-trained group (19.03±4.05 and 50.95±5.30), and the proportions of head nurses with high flexibility and high efficacy in the trained group (61.1% and 31.9%) were higher than those in the non-trained group (32.4% and 8.1%), and the differences above were statistically significant (P<0.05).
Conclusions
The training of the situational leadership theory can improve the application of theory to clinical nursing management and promote the head nurses’ flexibility and efficacy to accelerate their work enthusiasm and personal improvement. It can also promote team cohesion and sense of accomplishment by creating a positive team atmosphere to make the efficient usage of limited human resources.
As a complex system, the topology of human’s brain network has an important effect on further study of brain’s structural and functional mechanism. Graph theory, a kind of sophisticated analytic strategies, is widely used for analyzing complex brain networks effectively and comparing difference of topological structure alteration in normal development and pathological condition. For the purpose of using this analysis methodology efficiently, it is necessary to develop graph-based visualization software. Thus, we developed VisConnectome, which displays analysis results of the brain network friendly and intuitively. It provides an original graphical user interface (GUI) including the tool window, tool bar and innovative double slider filter, brain region bar, runs in any Windows operating system and doesn’t rely on any platform such as Matlab. When importing the user-defined script file that initializes the brain network, VisConnectome abstracts the brain network to the ball-and-stick model and render it. VisConnectome allows a series of visual operations, such as identifying nodes and connection, modifying properties of nodes and connection such as color and size with the color palette and size double slider, imaging the brain regions, filtering the brain network according to its size property in a specific domain as simplification and blending with the brain surface as a context of the brain network. Through experiment and analysis, we conclude that VisConnectome is an effective visualization software with high speed and quality, which helps researchers to visualize and compare the structural and functional brain networks flexibly.
In the context of actively coping with aging, China has introduced a series of health care integration policies. Using the advocacy coalition framework theory, this paper aims to analyze the process of health care integration policy changes in China from three dimensions: policy beliefs, external events and policy learning. The policy subsystem of health care integration in China includes two coalitions: top-down cascade promotion and bottom-up absorption and radiation. External events and policy learning triggered policy change, where policy learning included endogenous learning within the coalition and exogenous learning between the coalitions. A policy impasse occurs when the two advocacy coalitions are at odds, and policy brokers and professional forums can get rid of the policy impasse. In the process of policy change in China’s health care integration, the two major advocacy coalitions have reached a certain consensus. It is recommended to alleviate the problems in the integration of health care by strengthening the external factors in the change of health care policy, enhancing the policy learning in the change of health care policy, and making full use of the information resources in the change of health care policy, so as to promote the high-quality development of the integration of health care.
ObjectiveTo investigate the effect of positive family behavior support on emotional and behavioral problems in preschool children with epilepsy. Methods A total of 80 preschool epileptic children and their parents who were admitted to the Department of Neurology of our hospital from October 2022 to February 2023 were selected as the research objects, and were divided into experimental group and control group with 40 cases each by random number table method. The control group received neurology routine nursing, and the experimental group received positive family behavior support intervention based on the control group. The scores of family intimacy and adaptability scale, strengths and difficulties questionnaire, medication compliance and quality of life of epilepsy children were compared before and after intervention between the two groups. ResultsAfter intervention, the scores of strength and difficulty questionnaire in experimental group were lower than those in control group (P<0.05), and the scores of family intimacy and adaptability scale, quality of life and medication compliance in experimental group were higher than those in control group (all P<0.05). ConclusionThe application of positive family behavior support program can reduce the occurrence of emotional behavior problems, improve family closeness and adaptability, improve medication compliance, and improve the quality of life of preschool children with epilepsy.
Developmental and epileptic encephalopathy (DEE) is a group of diseases that severely affects the neurological development of children, characterized by frequent seizures and significant neurodevelopmental impairments. These diseases not only impact the quality of life of affected children but also impose a heavy burden on families and society. In recent years, the development of brain network theory has provided a new perspective on understanding the pathological mechanisms of DEE, especially the role of structural and functional brain networks in the process of epilepsy. This review systematically summarized the research progress of structural and functional brain networks in DEE, highlighted their importance in seizure activity, disease progression, and prognosis evaluation.
Currently, about one-third of patients with anti-epilepsy drug or resective surgery continue to have sezure, the mechanism remin unknown. Up to date, the main target for presurgical evaluation is to determene the EZ and SOZ. Since the early nineties of the last century network theory was introduct into neurology, provide new insights into understanding the onset, propagation and termination. Focal seizure can impact the function of whole brain, but the abnormal pattern is differet to generalized seizure. Brain network is a conception of mathematics. According to the epilepsy, network node and hub are related to the treatment. Graphy theory and connectivity are main algorithms. Understanding the mechanism of epilepsy deeply, since study the theory of epilepsy network, can improve the planning of surgery, resection epileptogenesis zone, seizure onset zone and abnormal node of hub simultaneously, increase the effect of resectiv surgery and predict the surgery outcome. Eventually, develop new drugs for correct the abnormal network and increase the effect. Nowadays, there are many algorithms for the brain network. Cooperative study by the clinicans and biophysicists instituted standard and extensively applied algorithms is the precondition of widely used clinically.
NetMetaXL is a macro command to conduct network meta-analysis in the frame of Microsoft Excel on basis of Bayesian theory. This macro command, which was officially launched in 2014, integrates data extraction and entry, analysis results output and graph plotting as a whole. Currently, this version contains enough optional models, and all operations are through menu and easy to conduct; however, it is appropriate only for the network meta-analysis based on dichotomous variables, which still has fairly a lot to be enhanced and improved. This article gives a brief introduction based on examples to implement network meta-analysis using NetMetaXL.
The artificial neural network has the ability of the information processing and storage, good adaptability, strong learning function, association function and fault tolerance function. The research on the artificial neural network is mostly focused on the dynamic properties due to fact that the applications of artificial neural networks are related to its dynamic properties. At present, the researches on the neural network are based on the hierarchical network which can not simulate the real neural network. As a high level of abstraction of real complex systems, the small world network has the properties of biological neural networks. In the study, the small world network was constructed and the optimal parameter of the small word network was chosen based on the complex network theory firstly. And then based on the regulation mechanism of the synaptic plasticity and the topology of the small world network, the small world neural network was constructed and dynamic properties of the neural network were analyzed from the three aspects of the firing properties, dynamic properties of synaptic weights and complex network properties. The experimental results showed that with the increase of the time, the firing patterns of excitatory and inhibitory neurons in the small world neural network didn’t change and the firing time of the neurons tended to synchronize; the synaptic weights between the neurons decreased sharply and eventually tended to be steady; the connections in the neural network were weakened and the efficiency of the information transmission was reduced, but the small world attribute was stable. The dynamic properties of the small world neural network vary with time, and the dynamic properties can also interact with each other: the firing synchronization of the neural network can affect the distribution of synaptic weights to the minimum, and then the dynamic changes of the synaptic weights can affect the complex network properties of the small world neural network.
With the range of application of computational biology and systems biology gradually expanding, the complexity of the bioprocess models is also increased. To address this difficult problem, it is required to introduce positive alternative analysis method to cope with it. Taking the dynamic model of the epidemic control process as research object, we established an evaluation model in our laboratory. Firstly, the model was solved with nonlinear programming method. The results were shown to be good. Based on biochemical systems theory, the ODE dynamic model was transformed into S-system. The eigen values of the model showed that the system was stable and contained oscillation phenomenon. Next the sensitivities of rate constant and logarithmic gains of the three key parameters were analyzed, as well as the robust of the system. The result indicated that the biochemical systems theory could be applied in different fields more widely.
Traditional sample entropy fails to quantify inherent long-range dependencies among real data. Multiscale sample entropy (MSE) can detect intrinsic correlations in data, but it is usually used in univariate data. To generalize this method for multichannel data, we introduced multivariate multiscale entropy into multiscale signals as a reflection of the nonlinear dynamic correlation. But traditional multivariate multiscale entropy has a large quantity of computation and costs a large period of time and space for more channel system, so that it can not reflect the correlation between variables timely and accurately. In this paper, therefore, an improved multivariate multiscale entropy embeds on all variables at the same time, instead of embedding on a single variable as in the traditional methods, to solve the memory overflow while the number of channels rise, and it is more suitable for the actual multivariate signal analysis. The method was tested in simulation data and Bonn epilepsy dataset. The simulation results showed that the proposed method had a good performance to distinguish correlation data. Bonn epilepsy dataset experiment also showed that the method had a better classification accuracy among the five data set, especially with an accuracy of 100% for data collection of Z and S.
